Abstract 3105: Computational integration with phage library to optimize clones of DEFB4A antibodies for cancer diagnostics and therapeutics

Abstract

Abstract DEFB4A, a human beta-defensin, functions as a potential cancer biomarker, with elevated expression observed in various cancers, making it a candidate for diagnostic and prognostic applications. Additionally, its role in immune modulation suggests that DEFB4A may hold promise as a target for novel cancer treatments, particularly in the context of immunotherapy. This study aimed to develop a stable and high-affinity DEFB4A antibody for clinical and pre-clinical use. To achieve this, a combination of high-diversity phage libraries and computation-assisted analysis was employed to generate single-chain variable fragments (ScFv) clones specifically targeting DEFB4A. This research presents a robust approach for the generation of DEFB4A-targeting single-chain variable fragments (ScFv) from a phage library characterized by a high diversity of 109 unique clones and high titer of 1013 cfu/mL. Computational optimization was then applied to enhance the affinity, stability, and expression of these candidates through rational design and virtual mutagenesis programs. The binding activity of the optimized mutants was validated using biophysical methods such as surface plasmon resonance (SPR) and thermal stability assays. The process showed a remarkable 100-fold increase in affinity and stability. Additionally, the selected ScFv clone was further employed in immunohistochemistry (IHC) to detect DEFB4A in breast, prostate, and gastric cancers, showing higher expression levels compared to initial clones. This study highlights the synergy of high-diversity phage libraries and computational methods in crafting DEFB4A antibodies with enhanced affinity, stability, and expression. The findings advance the field of antibody engineering, emphasizing the crucial integration of experimental and computational approaches. This work lays the groundwork for the development of high-performance biologics with diagnostic and prognostic applications across diverse cancer types. Furthermore, it demonstrates the possibility of integrating computational approaches in antibody development, offering the promise of expediting the process in the future. Citation Format: Yichen Guo, Jina Yom, Andy (Xi) Han, Zhaoying Guo, Eden Zewdu, Rachel Gonzalez, Tianli Qu, Bailey Gilmore, Xuan Liu, Wei Fu, Xiaomin Hu. Computational integration with phage library to optimize clones of DEFB4A antibodies for cancer diagnostics and therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3105.